Robust SEs in GLMMs
You have to be careful when modeling auto-correlation in a binary response. When using a Gaussian distribution it is possible to model the variance and correlation separately from the mean. No so for a Bernoulli distribution (or binomial or Poisson). In some sense the whole purpose of generalized linear models is to take into account that the variance of each response is determined by its mean in these distributions. glmmPQL is a wrapper around the lme function from the nlme package. But lme, which provides for modelling correlations, was not intended for this purpose. I personally don't think it would make sense to use a correlation function with a binary response. A preferred approach is to incorporate Gaussian-distributed random effects that have the desired auto-correlation pattern.
On Tue Nov 25 2014 at 11:58:08 AM Tim Meehan <tmeeha at gmail.com> wrote:
Hi Sharon, I just looked over a paper by Bolker et al. (2008. GLMMs: a practical guide for ecology and evolution. TREE). Turns out that while it is possible to model binary data with glmmPQL, it's not really recommended. Nonetheless, you might look for other options that involve modeling autocorrelation rather than correcting for it after the fact. Best, Tim On Tue, Nov 25, 2014 at 10:19 AM, Tim Meehan <tmeeha at gmail.com> wrote:
Hi Sharon, Take a look at glmmPQL in the MASS package. This function allows you to model a binary response, with random effects, and temporally and
spatially
correlated errors. If you model the correlations, there is less of a
need
for adjusting standard errors. Best, Tim On Sun, Nov 23, 2014 at 2:04 PM, Sharon Poessel <sharpoes at gmail.com> wrote:
When computing resource selection functions for animal telemetry data
with
a binary response variable, where the 1s represent animal location data, which are spatially and temporally correlated, and the 0s represent
random
locations, which are not correlated, it is recommended to calculate robust, or empirical, standard errors instead of using the model-based standard errors to account for this differing correlation structure. As far as I can tell, none of the glmm packages in R calculate these robust SEs.
Does
anyone know of a way to use glmms that calculate these? Thanks.
Sharon
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